BreakvarHeteroskedasticityTest

class BreakvarHeteroskedasticityTest(p_threshold=0.05, subset_length=0.3333333333333333, alternative='two-sided', use_f=True)[source]

Variance break test for heteroskedasticity.

Uses statsmodels.tsa.stattools.breakvar_heteroskedasticity_test to test whether the sum-of-squares in the first subset of the sample is significantly different than the sum-of-squares in the last subset of the sample.

The null hypothesis is of no heteroskedasticity.

In literature, this test is typically applied to residuals of a fitted model, but can be applied to any time series.

It can also be used as a weak test for non-stationarity, as hetoroskedasticity implies non-stationarity, but not vice versa.

Parameters:

p_thresholdfloat, optional, default=0.05: significance threshold to apply in testing for heteroskedasticity.
subset_lengthfloat, default=1/3: Length of the subsets to test.
alternativestr, ‘increasing’, ‘decreasing’ or ‘two-sided’, default=’two-sided’: This specifies the alternative for the p-value calculation.
use_fbool, optional: Whether or not to compare against the asymptotic distribution (chi-squared) or the approximate small-sample distribution (F). Default is True (i.e. default is to compare against an F distribution).

Attributes:

stationary_bool: whether the series in fit is homoskedastic according to the test, more precisely, whether the null hypothesis is rejected at p_threshold. Homoskedasticity is implied by stationarity, but not vice versa, therefore False implies non-stationarity, but True does not imply stationarity.
bh_statistic_float: Test statistic(s) H(h).
pvalue_float: p-value(s) of test statistic(s).

Examples

>>> from sktime.datasets import load_airline
>>> from sktime.param_est.stationarity import BreakvarHeteroskedasticityTest
>>>
>>> X = load_airline()
>>> sty_est = BreakvarHeteroskedasticityTest()
>>> sty_est.fit(X)
BreakvarHeteroskedasticityTest(...)

Methods

`check_is_fitted`([method_name])	Check if the estimator has been fitted.
`clone`()	Obtain a clone of the object with same hyper-parameters and config.
`clone_tags`(estimator[, tag_names])	Clone tags from another object as dynamic override.
`create_test_instance`([parameter_set])	Construct an instance of the class, using first test parameter set.
`create_test_instances_and_names`([parameter_set])	Create list of all test instances and a list of names for them.
`fit`(X[, y])	Fit estimator and estimate parameters.
`get_class_tag`(tag_name[, tag_value_default])	Get class tag value from class, with tag level inheritance from parents.
`get_class_tags`()	Get class tags from class, with tag level inheritance from parent classes.
`get_config`()	Get config flags for self.
`get_fitted_params`([deep])	Get fitted parameters.
`get_param_defaults`()	Get object's parameter defaults.
`get_param_names`([sort])	Get object's parameter names.
`get_params`([deep])	Get a dict of parameters values for this object.
`get_tag`(tag_name[, tag_value_default, ...])	Get tag value from instance, with tag level inheritance and overrides.
`get_tags`()	Get tags from instance, with tag level inheritance and overrides.
`get_test_params`([parameter_set])	Return testing parameter settings for the estimator.
`is_composite`()	Check if the object is composed of other BaseObjects.
`load_from_path`(serial)	Load object from file location.
`load_from_serial`(serial)	Load object from serialized memory container.
`reset`()	Reset the object to a clean post-init state.
`save`([path, serialization_format])	Save serialized self to bytes-like object or to (.zip) file.
`set_config`(**config_dict)	Set config flags to given values.
`set_params`(**params)	Set the parameters of this object.
`set_random_state`([random_state, deep, ...])	Set random_state pseudo-random seed parameters for self.
`set_tags`(**tag_dict)	Set instance level tag overrides to given values.
`update`(X[, y])	Update fitted parameters on more data.